Stroking in early mother-infant exchanges: The role of maternal tactile biography and interoceptive sensibility

Caress-like is a crucial component of caregiving and a key factor in mother-infant interactions. Mother’s experience of touch during her own childhood (i.e., tactile biography) has been found to be related to maternal actual use of caress-like touch (i.e., stroking) during mother-infant exchanges. Evidence also suggests that maternal interoceptive sensibility (i.e., self-perceived sensitivity to inner-body sensations) might be related to sensitive caregiving abilities. However, further empirical investigation is needed to understand to what extent tactile biography and interoceptive sensibility have an impact on mothers’ stroking when interacting with their infants. Using an online survey, this cross-sectional study explored the potential association between maternal tactile biography, interoceptive sensibility and use of touch for interaction with their own infants in a group of 377 Italian mothers (mean age = 33.29; SD = 4.79). We tested and compared a series of multivariate linear mediation models using maternal tactile biography as predictor, maternal use of affective touch as outcome variable and Multidimensional Assessment of Interoceptive Awareness (MAIA) subscale scores as mediators. We found that, if a mother had positive touch experiences in her own childhood, she may be more likely to use touch in a positive and nurturing way with her own infant (i.e., stroking). Furthermore, mothers’ interoceptive sensibility in the form of attention regulation, self-regulation and body listening mediates the association between their past experiences of positive touch and their use of caress-like touch in mother-infant exchanges. This study highlights that maternal tactile biography is directly associated with mothers’ use of caress-like touch and indirectly linked to it through the mediating role of interoceptive sensibility.


Introduction
This report resumed supplementary information for the manuscript titled "Maternal interoceptive awareness bridges the association between maternal tactile biography and her present caress-like touch behaviors".The overall aim of the present study was to explore the role played by interoception in the association between maternal tactile biography and maternal stroking during daily caregiver activities.In this regard, caress-like touch is known to represent a crucial component of caregiving in early life and constitutes a key factor with a significant impact on infant later-life outcomes.Recent findings also suggest that maternal interoceptive awareness, which is individual ability to perceive signals coming from the own body, may represent an important factor in supporting the quality of care.As maternal tactile biography has already been found related to maternal present use of stroking during mother-infant exchanges, the overall aim of the present study was to explore a possible role played by maternal interoception.In order to test this association,data analyses were run on a sample of 377 Italian mothers (mean age = 33.29;SD = 4.79) participating in an online survey.We tested and compared a series of multivariate mediation models setting maternal tactile biography as predictor, maternal actual use of caress-like as outcome and MAIA-subscales as mediators.Findings suggest that mothers' interoceptive awareness, in form of attention regulation, self-regulation and body listening, mediates the association between maternal past experiences of positive touch during childhood and present caress-like touch enacted in mother-infant exchanges.
Here, we will focus only on the statistical analysis.For theoretical aspects, study aims,experiment design, and results interpretation the reader can refer directly to the article.Analysis were conducted with the R software version 3.6.1 (R Core Team, 2018) and models were estimated using the R package lavaan (Rosseel, 2017).

Comparing original and current sample
Participants inclusion criteria a) be older than 18 years old and b) having had a full-term baby in the past 19 months, c) be an Italian native speaker.The survey was filled by 562 mothers.Participants with data missing from more than 25% of items were eliminated from subsequent analyses.Similarly, participants who agreed to participate and completed the 85% of items but did not fulfill the eligibility criteria were excluded from the final sample.
From the initial sample, n= 572 subjects were excluded.In order to explore if excluded and included mothers were comparable, we graphically explored the frequencies of gender, answers to yes/no questions about COVID-19 and the density of age and fear of COVID-19 (from 0 to 10) in the two groups figure S1.The two groups resulted comparable.
Figure S1: Comparing samples.Included label refers to participants who met the inclusion criteria.Excluded label refers to participants who did not met one ore more inclusion criteria.

DASS-21 cutt-off
The Depression, Anxiety and Stress Scale -21 Items (DASS-21; Bottesi et al., 2015) is a self-report scale developed to assess the levels of depression, anxiety and stress.The DASS-21 (Lovibond & Lovibond, 1995) consists of three self-report scales, each with 7 items rated on a 4-point Likert scale.Participants are asked to indicate to what extent a given statement applied to them over the past week (ranging from 1 "Does not apply" to 5 "Applies very much").For each DASS scale, cut-off scores were developed to define mild/moderate/severe/extremely severe scores.Recommended cut-off points are reported by Bottesi et al., 2015 and resumed here in table S1 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5   1 2 3 4

Model Comparison
In order to evaluate the research hypotheses about the role played by maternal interoception in the association between maternal tactile biography (i.e., TBQ-childhood and adolescents scores) and maternal actual use of caress-like touch in daily caregiver activities, a model comparison approach was used.Model comparison allows for the selection of the most plausible models given the data and a set of candidate models (McElreath, 2016).Models are compared in terms of statistical evidence (i.e.support by the data) using information criteria (Wagenmakers & Farrell, 2004), which enables the evaluation of models considering the trade-off between parsimony and goodness-of-fit (Vandekerckhove et al., 2015).In this regard, it is important to keep in mind that as the complexity of the model increases (i.e. more parameters) the fit to the data increases as well but generalizability (i.e.ability to predict new data) decreases (Pitt, Myung & Zhang, 2002).The researchers' aim is to find the best balance between fit and generalizability in order to describe, with a statistical model, the important feature of the studied phenomenon, but not the random noise of the observed data.Information criteria provide an estimate of the average deviance (i.e.error) of the model's ability to predict new data, thus lower values are interpreted as indications of a better model (McElreath, 2016).Given the structure of the data, a series of multivariate mediation models were set up and compared with with a null model and a main effect model (i.e., including only the predictor).In the light of what we said above, we used the AIC (Akaike, 1974) and Akaike weights (Anderson & Burnham, 2002) to compare tested models.
According to AIC criteria, the lower the value the better the model is at predicting new data, while for Akaike weights, which range from 0 to 1, the higher the value, the better the model is at describing data accurately (Vandekerckhove et al., 2015;Wagenmakers & Farrell, 2004).Moreover, based on AIC (Akaike, 1974) and AIC weights (Anderson & Burnham, 2002), we also explored which of the possible mediators had the greatest weight in explaining the link between maternal tactile biography (i.e., predictor) and maternal present stroking (i.e., outcome).All the models are controlled for maternal age.We remind the reader that Noticing and Not disturbing sub-scales were excluded from this comparison as omega values resulted .56 and .20 respectively, highlighting unacceptable performances of the scales.

Multivariate Linear Mediation Models
In this section we first specify all the multivariate mediation models tested.Subsequently we give the results of the model comparison.In order to read the following boxes, please note that parameters such as a, b1, d1, b2, d2, and so on, represent labels of coefficients in the regression equations.Each of these coefficients has a specific interpretation in the context of the model.
For instance, a represents the regression weight linking the predictor (i.e., TBQ child) to the latent variable (i.e., f1 picts).The b coefficients capture the direct paths between past positive experiences and present maternal tactile behaviors, while the d coefficients represent the effects of maternal past positive tactile experiences on the interoceptive sensibility sub scales.
The ind variables quantify the indirect effects, and sum ie and total provide comprehensive measures of indirect and total effects, respectively.

Overall Model
In the 3.1.1model, all mediators are included.All the other models derive from the 3.1.1model but test only one mediator at a time.The syntax of the 3.1.1model is reported below and it is called overall med.Please note that Eta genitore stands for maternal age.[1] 1 0 0 0 0 0 0 0 0 Overall, as it clearly emerges from AIC values, mediation models outperformed the overall, the null and the main-effect models (Table 3.2).Considering the weights, AIC weights were lower than 0.001 for all the models except for the model with not-worrying scores as mediator where AIC weights were very close to 1.00.Thus, among all the tested mediators, the model with not-worrying scores resulted as the best one.

Figure
Figure S2 represent relative frequencies distribution of items scores of the included instruments.Items' response values and frequencies are reported on the x and y axis respectively.

Fig. 3
Fig. 3 represents the frequency (and percentage) of missing values for each item of PICTS, TBQ and MAIA questionnaires.Given the low percentage of missing data, a listwise deletion strategy was performed, excluding subjects with missing values from the analyses.

Figure S3 :
Figure S3: Percentage of missing values for each item of PICTS, TBQ and MAIA questionnaires (n = 377).Every row represents an item of PICTS, TBQ and MAIA respectively.MAIA items are named here as VMCI. .